Radio frequency (RF)-based techniques are widely adopted for indoor
localization despite the challenges in extracting sufficient information from
measurements. Soft range information (SRI) offers a promising alternative for
highly accurate localization that gives all probable range values rather than a
single estimate of distance. We propose a deep learning approach to generate
accurate SRI from RF measurements. In particular, the proposed approach is
implemented by a network with two neural modules and conducts the generation
directly from raw data. Extensive experiments on a case study with two public
datasets are conducted to quantify the efficiency in different indoor
localization tasks. The results show that the proposed approach can generate
highly accurate SRI, and significantly outperforms conventional techniques in
both non-line-of-sight (NLOS) detection and ranging error mitigation.

本文提出了一种基于深度学习的方法来从射频测量数据中生成准确的软量信息（SRI），实现不同户内定位任务中的非直线视距检测和距离误差缓解，并在两种公共数据集的案例研究中进行了广泛的实验以量化效率。